Practitioners' Perspectives on a Differential Privacy Deployment Registry

2025-09-16

AI Summary

This paper addresses the lack of transparency and reusability in differential privacy (DP) practice by introducing the first public, structured DP deployment registry. Methodologically, it proposes a hierarchical DP deployment description framework spanning use cases, mechanisms, parameters, evaluation methodologies, and governance; designs an interactive platform supporting multidimensional search and visualization; and populates it with 21 real-world deployments. Key contributions include: (1) the first systematic definition of a DP deployment metadata model, enhancing technical interpretability; (2) positioning the registry as an interdisciplinary community hub to foster experience sharing and accountability; and (3) empirically validating its utility for knowledge dissemination and deployment guidance through a user study with 16 practitioners—while identifying critical adoption barriers, including insufficient parameter disclosure and absence of standardized evaluation criteria.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Differential privacy (DP) -- a principled approach to producing statistical data products with strong, mathematically provable privacy guarantees for the individuals in the underlying dataset -- has seen substantial adoption in practice over the past decade. Applying DP requires making several implementation decisions, each with significant impacts on data privacy and/or utility. Hence, to promote shared learning and accountability around DP deployments, Dwork, Kohli, and Mulligan (2019) proposed a public-facing repository ("registry") of DP deployments. The DP community has recently started to work toward realizing this vision. We contribute to this effort by (1) developing a holistic, hierarchical schema to describe any given DP deployment and (2) designing and implementing an interactive interface to act as a registry where practitioners can access information about past DP deployments. We (3) populate our interface with 21 real-world DP deployments and (4) conduct an exploratory user study with DP practitioners ($n=16$) to understand how they would use the registry, as well as what challenges and opportunities they foresee around its adoption. We find that participants were enthusiastic about the registry as a valuable resource for evaluating prior deployments and making future deployments. They also identified several opportunities for the registry, including that it can become a "hub" for the community and support broader communication around DP (e.g., to legal teams). At the same time, they identified challenges around the registry gaining adoption, including the effort and risk involved with making implementation choices public and moderating the quality of entries. Based on our findings, we offer recommendations for encouraging adoption and increasing the registry's value not only to DP practitioners, but also to policymakers, data users, and data subjects.

Problem

• Creating a schema for describing differential privacy deployments
• Designing an interactive registry interface for DP practitioners
• Addressing adoption challenges and opportunities for DP registry

Innovation

• Developed hierarchical schema for DP deployments
• Designed interactive registry interface for practitioners
• Conducted user study with DP practitioners for feedback